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Toward autonomous robotic containment booms: visual servoing for robust inter-vehicle docking of surface vehicles

机译:朝着自主机器人遏制繁荣:视觉伺服表面车辆的坚固型互动耦合

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Inter-vehicle docking is the problem of coordinating multiple robots to actively form and maintain physical contact. It is an important capability for autonomous surface vehicles (ASVs) and is an essential part of a wide class of missions. This article considers one such mission: the emergency response and environmental protection problem of containing a floating pollutant. We propose a solution in which multiple robots autonomously navigate so as to surround the surface matter. Before doing so, the robots dock with one another to secure specialized attachments designed to ensnare the contaminant. We describe the prototypical physical robot system developed to perform this task, and we detail the system architecture, sensing and computational hardware, control system, and visual processing pipeline. While employing multiple ASVs maximizes spatial reconfigurability, it depends on the inter-robot docking capabilities being particularly reliable. But achieving robust docking is a significant technical challenge because the water continually induces external disturbances on the control system. These disturbances are non-stationary and almost impossible to predict for unknown environments. Our system relies primarily on visual servoing within a control framework in which a variety of sensors are fused. Accurate disturbance measurements are obtained through traditional sensor modeling and filtering techniques. As the environment is a priori unknown, varies from trial to trial, and has proven difficult to model, we apply a model-free reinforcement learning algorithm, SARSA(lambda), along with specialized initial conditions which ensure stable operation, and an exploration guidance approach that increases the speed of convergence. We adopt a two-loop control scheme for visual servoing to successfully make use of feature descriptors with various (and variable) computational times. We demonstrate this approach to the docking problem with autonomous ground vehicles and ASVs. The results from several situations are compared, showing that disturbance rejection coupled with SARSA(lambda) is an effective approach.
机译:车间对接是协调多个机器人的问题,以主动形成和维持身体接触。它是自主地面车辆(ASV)的重要能力,是广泛任务的重要组成部分。本文考虑了一个这样的使命:含有浮动污染物的紧急响应和环境保护问题。我们提出了一种解决方案,其中多个机器人自主地导航以围绕表面物质。在这样做之前,机器人彼此停靠​​,以确保旨在留下污染物的专用附件。我们描述了开发执行此任务的原型物理机器人系统,我们详细介绍了系统架构,感应和计算硬件,控制系统和视觉处理管道。在采用多个ASV的同时,最大限度地提高空间重新配置性,这取决于机器人间对接功能特别可靠。但是,实现强大的对接是一个重要的技术挑战,因为水不断地在控制系统上诱导外部干扰。这些扰动是非静止的,几乎不可能预测未知的环境。我们的系统主要依赖于在控制框架内的可视伺服,其中各种传感器被融合。通过传统的传感器建模和过滤技术获得精确的干扰测量。由于环境是一个先知的未知,从试验中变化,并且已经难以模仿,我们应用了一种无模型的强化学习算法,萨拉(Lambda),以及专业的初始条件,确保运行稳定,以及勘探指导提高收敛速度的方法。我们采用了一个双环控制方案,用于Visual ServoIting以成功地利用具有各种(和变量)计算时间的特征描述符。我们展示了这种与自主地面车辆和ASV的对接问题的方法。比较了几种情况的结果,表明与Sarsa(Lambda)耦合的干扰排斥是一种有效的方法。

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